SIMULATION (MONTE CARLO)

In today’s world, risk analysis occur a state role in part of every decision we make. We are usually confronted with uncertainty. And even though we have unprecedented access to information, we can’t accurately predict the future. Monte Carlo simulation provides people with seeing outcomes which are likely possible results of your decisions. In order to make better decision, it helps you to assess the effect of risk.

Monte Carlo simulation is a mathematical method that provides people with accounting the risk in quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil and gas, transportation, and the environment.

Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of value a probability distribution or any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values.

Using Monte Carlo simulation has lots of advantages in estimating and determining of a project. Firstly, it provides probabilistic results. They will show both the results which could happen and how likely each outcomes. Secondly, Monte Carlo simulation includes large variety of data. And these data helps to create a graph of results. Third one is about sensitivity analysis. With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most.  In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results. Other advantage is scenario analysis. In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis. The other one is related to correlation of inputs. In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables.  It’s important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.